Epigenetic Alterations of Repeated Relapses in Patient-matched Childhood Ependymomas

Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.

The authors rightfully focus on the question of why many ependymoma tumors recur. To uncover new molecular mechanisms of tumor recurrence, the authors decided to closely examine genome-wide DNA methylation patterns and generated a rich data set of gene expression and reduced representation bisulfite sequencing (RRBS). In line with previous reports, DNA methylation remains very stable throughout disease progression. It is really fascinating how stable the DNA methylation profiles are and even converge during repeated relapses. At the same time, this property somewhat limits the space for novel insights into ependymoma tumor biology by focusing on DNA methylation.
The authors have extensively used many different standard methods to analyze DNA methylation, such as the analysis of DMCs, DMRs, non-CpG methylation, and the association of DNA methylation patterns near transcription start sites with gene expression changes. The results are a detailed encyclopedic review of DNA methylation patterns in primary and relapse PFA and RELA ependymoma tumors.
Despite the limited new insights into the molecular mechanisms that promote tumor recurrence and the lack of experimental validations, the study has a high impact as a resource due to the invaluable sample cohort and the epigenetic data obtained. Please find detailed comments below. 1) "Driver" methylation events are defined, but there is no functional evidence of these regions' tumorigenicity. PDX models are presented as validation, but these models only show that the tumor is tumorigenic, not that DNA methylation causes that tumorigenicity. Functional validations are likely beyond the scope of the study, which is ok, but DNA methylation needs to be consistently described as a possible driver rather than a driver.
2) I understand that abnormal DNA methylation present in primary tumors is defined by a comparison to the normal cerebellum/ cerebra tissue. This needs to be described more clearly in the main text.
3) A statistical problem is the treatment of dependent samples as independent. Eg., the authors claim to have found a set of methylation markers for RELA EPN relapse, but this set is dominated by markers specific to a single patient's multiple relapses. Given the rarity of EPN and their small sample size, perhaps this is unavoidable. However, these markers need to be tested on an independent dataset of EPN tumors. 4) Page 6 "This finding is new and they demonstrated the maintenance of EPN molecular subtypes during repeated relapses (>=2) despite years of chemo-and/or radiation therapies (Fig 1A)." This is very impressive especially for the serial analysis. However, it has previously been shown that DNA methylation remains highly similar in the relapse compared to primary ependymomas. 5) Page 6 "For non-CpG methylation, the primary and recurrent tumors were found to have a dramatically decreased mCpA levels in both RELA and PFA tumors (Fig. S1F)." There is a striking difference of non-CpG methylation in the tumors as compared to the normal cerebellum/ cerebrum. Can the authors speculate what that means? Are these the same regions defined as abnormal DMCs/ DMRs? 6) It is very impressive to see that late recurrent tumor(s) are much more likely to develop as PDX tumors. However, it's not evident that "convergent epigenetic reprogramming" promotes "tumorigenicity in EPN relapses". Gain of chromosome 1q has been identified as a genetic marker associated with recurrence and poor survival in PFA ependymoma tumors. For PFA ependymoma, the emergences of 1q gains might drive tumorgenicity instead of DNA methylation. Have the authors attempted to identify chr1q copy number status using the RRBS data and are there more frequent chr1q gains in the PFA relapses that grow out as PDX tumors? 7) "These DMCs persisted from the primary tumors to late relapses, thereby constituting novel DNA methylation driver signatures of EPN relapse." What we know is that these CpGs are consistently more/ less methylated in EPN compared to normal tissue but that doesn't mean those drive EPN relapse. It could be that whole cerebrum und whole cerebellum are in a different differentiation status than the actual EPN precursor cells and there might still be something completely different that drives tumor progression. Fig.3 F: x-and y-axes need a separate legend for RELA (Primary or recurrentcerebellum/ cerebra) 9) "Most of the DMR regulated genes were newly discovered for EPN relapses. Their potential in as relapse driver genes was further enhanced by the fact that many of them, including CACNA1H, SLC12A7, CSPG4, RARA in RELA, and HSPB8, ITGB4, FAT1 in PFA tumors, have previously been associated with human cancers." Many of these gene have previously been described in ependymoma, such as CACNA1H and WEE1, and some of them were experimentally validated as ependymoma tumor dependency genes. 10) "longitudinal analysis of consecutive, serially relapsing patient tumor samples will enable the separation of epigenetic driver(s) from transient random alterations". Just because a mutation is conserved in subsequent relapses does not make it pathogenic; this would imply that non-pathogenic passenger mutations are somehow lost in subsequent relapses, which is not true.

REVIEWER COMMENTS
Reviewer #1, expertise in ependymomas, genomics, mouse models of brain tumours (Remarks to the Author): In the present manuscript, Zhao et al. performed single-base resolution DNA methylation analysis and report convergency of DNA methylation profiles during the progression of tumor recurrences. They identified differentially expressed genes which are regulated by driver DMRs and booster DMRs. Moreover, they also identified potential relapse predictors in RELA and PFA tumors. In general, this manuscript provided a new insight into epigenetic regulation of repeated recurrences in ependymoma. In general, the data itself for study mechanisms underlie recurrence in EPN is valuable. However, the findings are preliminary and most of the statements in this study are derived from bioinformatic results, no strong experimental evidence is provided. Several points that need to be addressed are listed below and hope those comments would be helpful.

Response:
We appreciate the insightful comments from the reviewers. They really are very helpful and surely make our manuscript stronger.
Major concerns: 1. The authors determined two subtypes of EPNs (PF and RELA) using the Phylogeny tree and PCA analysis of the methylation and RNA-seq dataset, the results indicated that these two subsets are indeed distinctive. However, there is no information of the brain regions where the samples were collected, the authors should ensure the heterogeneity are not from the brain region differences, the compassion is only reasonable when the samples are from the same/similar brain regions.

Response:
We agree with the reviewer that the location of ependymoma is very important. We originally summarized the clinical information in Supplemental Table 1 (which was clearly not the best way of data presentation). Additionally, we brought Table 1 into the main text and updated it by including the 4 normal brain tissues obtain from autopsy. The updated Table 1 is attached. And, all the PFA tumors were located in the posterior fossa (cerebella) and RELA in cerebral hemisphere.
2. In Fig1A, the author reported "Among the 5 patients whose primary tumors (n=1 patient: PFA3) or early recurrent tumors (n=4 patients: RELA1, PFA5, PFA2, PFA1) did not form xenografts, their late recurrent tumor(s) formed PDOX tumors". It would be more convincing to provide experimental images besides the schematic diagram shown in Fig1A.

Total Follow Up Duration
Response: Yes, we agree with the reviewer that this is a very good approach. We have thus relocated the tumor histology (original Fig 2C) to Figure 1B, and added five sets of H&E stained whole mouse brain sections together with three enlarged (10x) images to show the location of intra-cerebellar (ICb) and intracerebral (IC) xenograft tumors of early (small) and late (big) growth as well as a representative image showing CSF spread (please see the inserted images below for Fig 1A and 1B). Our plan is to report detailed cellular and molecular characterizations of these models in a separate paper in the near future.
3. Fig.1E-F, it is not very clear how the genes were selected to show in the heatmaps. Are they representative enough to convey the message claimed in the main text? Response: In Figure 1E-F, these signature genes were selected from a previously published database GSE64415, which is primarily composed of primary tumors of ependymoma. The genes themselves were identified/defined by the authors of this database, and the levels of expression were derived from our dataset. Our goal is to make use of the independent set of data to validate our results.
4. Please elaborate on the saying "This finding is new", there is no enough context for readers to buy it. Response: We believe the reviewer was referring to this sentence in page 6 "This finding is new and they demonstrated the maintenance of EPN molecular subtypes during repeated relapses (>=2) despite years of chemoand/or radiation therapies (Fig 1A)." To avoid ambiguity, we revised it to the following on Page 6: These set of data demonstrated that the maintenance of EPN molecular subtypes during repeated relapses (>=2) during years of chemo-and/or radiation therapies. (Fig 1A) 5. In Figure2a Fig 1E and 1F).
7. In Fig3, the author claimed "the strong negative correlations between the DMRs and their target genes indicated a causative role of the DMR drivers in regulating the gene expressions". This statement is overstated and not supported by experimental evidence. Response: We agree and replaced "causative" to "potential regulatory".
8. The definition of driver and booster is not solid enough to me, the conclusion inferred from them is therefore suspicious. Can the authors provide some reference or a third-party method to prove that the DMCs "that persisted in all the recurrences may have sustained (and boosted) tumor relapse and contributed to the increased tumorigenicity" indeed have a booster role?
Response: 1) We agree with the reviewer that, strictly speaking, a driver and booster should be validated by strong functional studies. Although we did not perform such functional study on single or selected genes, our data were generated from functionally (and clinically) validated tumor samples and provided a panel of genes (many of them, not all, novel in EPN) for detailed functional studies. 2) Since our data were derived from clinically-proven recurrences, we performed analysis to identify consistent DNA methylation changes (candidate drivers and boosters) is to differentiate them from stochastic DNA methylation alterations. And, yes, we did find a similar approached to define/identify candidate drivers. Dan A Landau et al developed a statistical framework-MethSig to accounting stochastic DNA hypermethylation rate across the genome and between patients to infer DNA methylation drivers (PMID: 33972312), which required large number of samples. The advantage of our datasets is that we leveraged multiple recurrent EPN samples derived from the same patients to filter out stochastic DNA methylation alterations along relapse. Since tumorigenicity has been recognized as a useful/reliable assay in evaluating tumor malignancy, our identification of those DMCs persisted in the established PDOX tumors provided, at least partially, the functional evaluation of these candidate drivers/boosters, albeit this strategy has not been widely used due to high demand of time and effort. And, we do agree that these DMCs should be defined as possible or candidate boosters, and that is why we described them as "….may have sustained (and boosted)….".
9. In Figure4C, could the author also show the biological enrichments of the DEGS that were negatively correlated with DNA methylation for RELA and PFA. What are the deep implications of these genes? Response: This is something we tried, but our effort was limited by the small number of gene to yield a significant result in the enrichment analysis. At the same time, this relatively small gene list also provided us with a reasonable foothold for near future functional analysis (as therapeutic target or diagnostic marker) of the target genes in a manageable fashion. We addressed this in page 12: Although the small number of dysregulated genes limited our capacity of detailed biological enrichment analysis, our discovery of their potentially new roles in promoting EPN relapses is exciting and warrants future functional validation and drug development.
10. In Fig5, the author reported several potential relapse predictors for in RELA and PFA tumors. As mentioned in the text, the sample size is relatively small. Then, how would the number of identified relapse predictors change as sample size changes? Would the number of candidates DMCs decrease as the sample size increases? If it's the case, how reliable the predictors are, since there could be large sample variation.
Response: This is a great question. Our effort of identifying relapse predicators is primarily driven by the clinical needs to prevent over-treatment of ependymomas (with unnecessary toxicity) that will not/or with low probabilities of relapse, or under-treated (to suffer from relapse) of tumors that will recur. The predictors we identified is aimed to provide a proof-of-principle to ignite additional efforts in the field of ependymoma studies so that large number of clinically annotated samples can be accumulated. And, yes, we do anticipate that the predictors will change as sample size increases. While it is difficult to predict if the DMCs will increase or decrease when the sample size increases (because it may increase the power to detect more DMCs), it is still highly desired that the number of candidate DMCs will decrease, especially following a progressively deeper understanding of the molecular subtypes of ependymomas and increased stringency. We added the following discussion in Page 16: Despite sample variations of candidate predicators, it is highly desired that the number of predictors for EPN recurrence for each current or future molecular subtypes will decrease or be clinically applicable.
1. In Figure1c, it makes more sense to show the column dendrogram as well. In Figure1d, there are two RELA samples are closed to the Cerebrum samples, could the authors show the names of the two samples, and give an explanation of this situation? In Figure1EF, how the authors determine the signature genes? Response: Yes, a colum dengdrogram is provided as Suplemental Fig 1D. As for the two samples in Fig 1D, the two RELA samples were RELA1-R3 and RELA3-R1. They did not form xenograft, and RELA1-R3 was obtained <4 months from RELA1-R2. Their closer proximity to the normal cerebral tissues (at DNA methylation) appears to suggest the contamination of peri-tumor normal brain/scar tissues in the surgical samples.
As for the signature genes In Figure1EF (also commented by Reviewer #1), they were extracted from published dataset GSE64415, which is primarily composed of primary tumors of ependymoma. The genes themselves were identified/defined by the authors of this database, and the levels of expression were from our dataset.
2. In figure1a, how to explain the correlation value decrease for the RELA2 between the 1st and 2nd relapsing? Response: We believe the reviewer was referring to Figure 2A. We wish to thank the reviewer for pointing this out. It is interesting to see the decreased correlation value in RELA2, and that is why we pointed it out with an arrow. When these two relapsed tumors are compared, we can see from Figure 1A that RELA2-R1 was tumorigenic, but RELA2-R2 failed to form xenograft, which is different from other late recurrent tumors that displayed stronger tumorigenicity. While tumorigenicity can be affected by multiple factors, the reduction of PDOX formation form RELA R1 to R2 may indicate tumor tissue sampling differences (normal tissue contamination?) in addition to biological alterations.  Figure 2D (Fig  s2D).  5. could the authors give explain the deep implication of the number of DEG differences in the upset plots?

Scale bar in
Response: We wish to thank the reviewer to point this out. We were excited about this comprehensive plot (that provided multi-parameters of important data) and added some more detailed description in the figure legend to better describe the graph: identification of 1q gain in PFA patient and PDOX tumors (Fig. s3). To supplement this approach, we applied FISH and detected 1q gain in paraffin sections of 3 sets of patient and xenograft tumors of PFA ependymoma (Fig  2C), suggesting that 1q gain in patient tumors were preserved in the PDOX tumors as well. These data support the analysis of additional PFA tumors, both tumorigenic and non-tumorigenic, to establish the role of 1q gain in PFA tumorigenicity.
In Discussion on page 17: 7) "These DMCs persisted from the primary tumors to late relapses, thereby constituting novel DNA methylation driver signatures of EPN relapse." What we know is that these CpGs are consistently more/ less methylated in EPN compared to normal tissue but that doesn't mean those drive EPN relapse. It could be that whole cerebrum und whole cerebellum are in a different differentiation status than the actual EPN precursor cells and there might still be something completely different that drives tumor progression. Response: Yes, we agree that it is possible the signatures can be associated with different differentiation status or developmental stage of the childhood brains. However, our patients aged from 2-10 at diagnosis and their relapses occurred from 1-13 years. Their normal brains (cerebral and cerebellar) have undergone significant changes. Therefore, we inserted the following sentences in Discussion on page 14: However, childhood brains are often in different differentiation status, e.g., from 2-10 year old as in our cohorts. When combined with different time frame of recurrence, ranging from 1-13 years, some of the candidate DMC drivers may be attributed to the patient specific-differentiation status of cerebrum and cerebellum. 8) Fig.3 F: x-and y-axes need a separate legend for RELA (Primary or recurrent -cerebellum/ cerebra)